# Semantic Segmentation Alternative Technique: Segmentation Domain Generation

Source: [Semantic Segmentation Alternative Technique: Segmentation Domain Generation](<https://www.sota2.com/research/paper/68fa6a571e209131cea2791a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:22:37.170Z

Authors: Ana-Cristina Rogoz, Radu Muntean, Stefan Cobeli

Published: 2021-07-06T10:34:55.000Z

## Abstract

Detecting objects of interest in images was always a compelling task to automate. In recent years this task was more and more explored using deep learning techniques, mostly using region-based convolutional networks. In this project we propose an alternative semantic segmentation technique making use of Generative Adversarial Networks. We consider semantic segmentation to be a domain transfer problem. Thus, we train a feed forward network (FFNN) to receive as input a seed real image and generate as output its segmentation mask.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2107.02525>)

[PDF](<https://arxiv.org/pdf/2107.02525.pdf>)